Merge pull request #284069 from laujan/acrolinx

acrolinx updates

Author: prmerger-automator[bot]

articles/ai-services/document-intelligence/concept-read.md

@@ -5,8 +5,6 @@ description: Extract print and handwritten text from scanned and digital documen
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 08/07/2024
 ms.author: lajanuar
@@ -135,7 +133,7 @@ POST /documentModels/prebuilt-read:analyze?output=pdf
 202
 ```
 
-Poll for completion of the `Analyze` operation. Once the operation is complete, issue a `GET` request to retrieve the PDF format of the `Analyze` operation results .
+Poll for completion of the `Analyze` operation. Once the operation is complete, issue a `GET` request to retrieve the PDF format of the `Analyze` operation results.
 
 Upon successful completion, the PDF can be retrieved and downloaded as `application/pdf`. This operation allows direct downloading of the embedded text form of PDF instead of Base64-encoded JSON.
 

articles/ai-services/document-intelligence/faq.yml

@@ -52,7 +52,7 @@ sections:
         answer: |
           **Yes.**
 
-          Document Intelligence now includes [custom generative](concept-custom.md) a new type of extraction model that uses Generative AI and large language models (LLMs) to extract fields from documents. In the past you've had to use a RAG (retrieval augmented generation) pattern to extract fields. The new model provides high quality results with a single API call.
+          Document Intelligence now includes [custom generative](concept-custom.md) a new type of extraction model that uses Generative AI and large language models (LLMs) to extract fields from documents. In the past, you used a RAG (retrieval augmented generation) pattern to extract fields. The new model provides high quality results with a single API call.
           You can also use a document generative AI solution to chat with your documents (RAG), generate captivating content from those documents, and access Azure OpenAI Service models on your data.
 
           - With Azure AI Document Intelligence and Azure OpenAI combined, you can build an enterprise application to seamlessly interact with your documents using natural language. You can easily find answers, gain valuable insights, and generate new and engaging content from existing documents.
@@ -64,9 +64,9 @@ sections:
         answer: |
           **Yes.**
           
-          Document Intelligence can provide the building blocks to enable semantic chunking. Semantic chunking is a key step in retrieval-augmented generation (RAG) to ensure context dense chunks and relevence improvement.
+          Document Intelligence can provide the building blocks to enable semantic chunking. Semantic chunking is a key step in retrieval-augmented generation (RAG) to ensure context dense chunks and relevance improvement.
 
-          - Document Intelligence provides a layout model that provides an visual decomposition of the document into lines, paragraphs, sections, headers and footers.
+          - Document Intelligence provides a layout model that provides a visual decomposition of the document into lines, paragraphs, sections, headers, and footers.
 
           - You can then choose to retrieve the results in markdown format, to further chunk the document on section or paragraph boundaries.
 
@@ -295,11 +295,11 @@ sections:
 
         Although training is free for all custom generative and custom template models, creating the training dataset for all models requires running the Layout model on the training documents. Customers are responsible for this cost. 
         
-        Custom generative models also rely on the auto label feature to speed up the generation of the labeled dataset. There is a cost associated with this action. While the build operation for template and generative models is free, creating the labeled dataset can result in some minimal costs.
+        Custom generative models also rely on the auto label feature to speed up the generation of the labeled dataset. There's a cost associated with this action. While the build operation for template and generative models is free, creating the labeled dataset can result in some minimal costs.
 
-        Custom neural models have a limit on the number of models/the amount of time that models can be trained for free. The first 10 hours of training are free. If training a single model for longer than 10 hours or training multiple models that exceed the 10 hour limit, you will need to enable paid training by setting a training budget. See [training a custom neural model](concept-custom-neural.md) for details. 
+        Custom neural models have a limit on the number of models/the amount of time that models can be trained for free. The first 10 hours of training are free. If training a single model for longer than 10 hours or training multiple models that exceed the 10 hour limit, you need to enable paid training by setting a training budget. See [training a custom neural model](concept-custom-neural.md) for details. 
         
-        For v3.0 or v3.1 models the paid training tier only applies to additional models, the training time per model is not configurable.
+        For v3.0 or v3.1 models the paid training tier only applies to added models, the training time per model isn't configurable.
 
   - name: Storage account
     questions:

articles/ai-services/document-intelligence/whats-new.md

@@ -57,7 +57,7 @@ The Document Intelligence [**2024-07-31-preview**](/rest/api/aiservices/document
 * [🆕 US Tax model](concept-tax-document.md)
   * New unified US tax model that can extract from forms such as W-2, 1098, 1099, and 1040.
 * 🆕 Searchable PDF. The [prebuilt read](concept-read.md) model now supports [PDF output](concept-read.md#searchable-pdf)  to download PDFs with embedded text from extraction results, allowing for PDF to be utilized in scenarios such as search copy of contents.
-* [Layout model](concept-layout.md) now supports improved [figure detection](concept-layout.md#figures) where figures from documents can now be downloaded as an image file to be used for further figure understanding. The layout model also features improvements to the OCR model for scanned text targeting improvements for single characters, boxed text and dense text documents.
+* [Layout model](concept-layout.md) now supports improved [figure detection](concept-layout.md#figures) where figures from documents can now be downloaded as an image file to be used for further figure understanding. The layout model also features improvements to the OCR model for scanned text targeting improvements for single characters, boxed text, and dense text documents.
 * [🆕 Batch API](concept-batch-analysis.md)
   * Document Intelligence now adds support for batch analysis operation to support analyzing a set of documents to simplify developer experience and improve efficiency.
 * [Add-on capabilities](concept-add-on-capabilities.md)